In the previous articles, we have looked at a regression problem and a binary classification problem. Let's now look at another common supervised learning problem, multi-class classification.

The staple training exercise for multi-class classification is the MNIST dataset, a set of handwritten roman numerals, while particularly useful, we can spice it up a little and use the Kannada MNIST dataset available on Kaggle.

The Kannada language is spoken in southern regions of India, by around 45 million people, and compared to roman numerals provides the advantage of being a lot less familiar to most people and also provides a little extra challenge due to similarity between some of its numerals.

If you are running the GPU version of Tensorflow, it's always nice to check that the GPUs are in fact available. Particularly with Convolutional Neural Network (or CNN for short) GPUs can speed up your training process up to 100x. Let's make sure that GPU power is at our fingertips.

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